We then performance NAS on our proposed new search space as described in Section . We use the same multi-objective reward mechanism as in [1]}. The multi-objective reward combines accuracy and latency as \(ACCURACY(m) \times \left[ \frac{LATENCY(m)}{Target}\right]^w\)  to approximate the Pareto-optimal results on both accuracy and latency. The factor \(w\)  decides the weight of latency in the reward. We re-calibrate the factor \(w\)  to make the reward design suitable for TPUv3 and GPUv100. Particularly, we use a larger weight factor \(w = -0.09\)  because model accuracy is less sensitive to latency variations on TPU and GPU platforms than on mobile platforms (original \(w = -0.07\)  in [1]}). We choose the multiplicative objective function form, the same form as used in the baseline EfficientNet, to ensure fair comparisons. Different objective function forms, such as additive forms[3]}, can potentially produce even better results, and we will investigate in future work.
